相关实验视频

Updated: May 23, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

一个高效和稳健的特征选择方法,基于对位测量和邻居意识模型

Kehua Yuan, Duoqian Miao, Hongyun Zhang

    IEEE transactions on neural networks and learning systems
    |May 21, 2025
    PubMed
    概括

    这项研究引入了一种新的模糊粗略设置方法,用于强大的特征选择. 它通过考虑多个层次的特征相互作用来提高抗噪声和分类准确性.

    更多相关视频

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    616
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    6.9K

    相关概念视频

    Choosing Between z and t Distribution01:25

    Choosing Between z and t Distribution

    The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
    2.7K
    Quantifying and Rejecting Outliers: The Grubbs Test01:02

    Quantifying and Rejecting Outliers: The Grubbs Test

    Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
    1.4K
    z Scores and Unusual Values01:07

    z Scores and Unusual Values

    The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
     This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
    9.3K
    Introduction to z Scores01:05

    Introduction to z Scores

    A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
    z scores...
    319
    What Are Outliers?01:12

    What Are Outliers?

    Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
    The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
    3.6K
    Expected Frequencies in Goodness-of-Fit Tests01:19

    Expected Frequencies in Goodness-of-Fit Tests

    A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
    2.5K